Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games

A major difficulty in a recommendation system for groups is to use a group aggregation strategy to ensure, among other things, the maximization of the average satisfaction of group members. This paper presents an approach based on the theory of noncooperative games to solve this problem. While group members can be seen as game players, the items for potential recommendation for the group comprise the set of possible actions. Achieving group satisfaction as a whole becomes, then, a problem of finding the Nash equilibrium. Experiments with a MovieLens dataset and a function of arithmetic mean to compute the prediction of group satisfaction for the generated recommendation have shown statistically significant results when compared to state-of-the-art aggregation strategies, in particular, when evaluation among group members are more heterogeneous. The feasibility of this unique approach is shown by the development of an application for Facebook, which recommends movies to groups of friends.

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